文献类型： 外文期刊

作者： Liu, Weiming ¹ ; You, Lanqing ¹ ; Wang, Sheng ¹ ; Li, Jie ⁴ ; Chen, Zhiyong ¹ ; Si, Buchun ⁵ ; Iqbal, Yasir ¹ ; Xue, Shuai ¹ ; Fu, Tongcheng ¹ ; Yi, Zili ¹ ; Li, Meng ¹ ;

作者机构： 1.Hunan Agr Univ, Coll Biosci & Biotechnol, Hunan Prov Key Lab Crop Germplasm Innovat & Utiliz, Changsha 410128, Peoples R China

2.Hunan Agr Univ, Natl Energy R&D Ctr Nonfood Biomass, Hunan Branch, Changsha 410128, Peoples R China

3.Hunan Agr Univ, Coll Biosci & Biotechnol, Hunan Engn Lab Miscanthus Ecol Applicat, Changsha 410128, Peoples R China

4.Hunan Acad Agr Sci, Hunan Inst Agr Informat & Engn, Hunan Intelligent Agr Engn Technol Res Ctr, Hunan Ind Technol Basic Publ Serv Platform, Changsha 410125, Peoples R China

5.China Agr Univ, Coll Water Resources & Civil Engn, Minist Agr & Rural Affairs, Key Lab Agr Engn Struct & Environm, Beijing 100083, Peoples R China

关键词： Miscanthus spp.; biomass quality traits; microcrystalline cellulose; cellulose nanocrystals; machine learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.3； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 14 卷 6 期

页码：

收录情况： SCI

摘要： Miscanthus spp. has been regarded as a promising industrial plant for the sustainable production of bio-based materials. To assess its potential for microcrystalline cellulose (MCC) and cellulose nanocrystals (CNCs) production, 50 representative clones of M. sinensis and M. floridulus were selected from a nationwide collection showcasing the extensive diversity of germplasm resources. Descriptive analysis indicates that the dry biomass weight of M. floridulus is advantageous whereas M. sinensis demonstrates higher MCC and CNCs yields as well as a smaller CNCs particle size. Correlation analyses indicated that MCC yield is solely influenced by the cellulose content whereas the yield of CNCs is affected by both the cellulose content and CrI. Comparative analyses of the chemical composition, physical features (degree of polymerization, crystalline index, particle size distribution and zeta potential), and scanning electron microscopy indicated that the MCC and CNCs extracted from M. sinensis and M. floridulus exhibited remarkable stability and quality. Additionally, the CNCs derived from M. sinensis and M. floridulus exhibited a distinctive ball-shaped structure. Notably, machine learning has demonstrated its efficacy and effectiveness in the high-throughput screening of large populations of Miscanthus spp. for predicting the yield of MCC and CNCs. Our results have also laid the theoretical foundation for the exploration, cultivation, and genetic breeding of M. sinensis and M. floridulus germplasm resources with the purpose of MCC and CNCs preparation.